def get_lambda_permutation_loss(labels, predicted_scores):
tmp_labels = tf.cast(tf.squeeze(labels), tf.int32)
y, idx = tf.unique(tmp_labels)
which_rank = tf.shape(y)[0] - 1
which_rank = tf.cast(which_rank, tf.float32)
raw_pairwise_label_scores = scores.get_pairwise_scores(labels)
mask = masks.list_mask(raw_pairwise_label_scores)
# calculate delta_z
gains = 2**labels - 1
raw_pairwise_scores = scores.get_pairwise_scores(predicted_scores)
score_mask = masks.list_mask(raw_pairwise_scores)
ranks = tf.reduce_sum(score_mask, axis=1)
ranks = tf.reshape(ranks, [-1, 1])
log_2 = tf.log(tf.constant(2.0, dtype=tf.float32))
cg_discounts = tf.log(ranks + 2.0) / log_2
cg_discounts = tf.reshape(cg_discounts, [-1, 1])
raw_pairwise_discounts = tf.abs(scores.get_pairwise_scores(cg_discounts))
raw_pairwise_gains = tf.abs(scores.get_pairwise_scores(gains))
delta_z = raw_pairwise_gains
delta_z = tf.multiply(raw_pairwise_discounts, raw_pairwise_gains)
abs_pairwise_scores = tf.abs(raw_pairwise_scores)
delta_z = tf.divide(delta_z, abs_pairwise_scores + 1e-7)
# max_dcg = tf.reduce_sum(math_fns.cal_idcg_ks(labels, 10))
max_dcg = tf.reduce_sum(
math_fns.cal_idcg_ks(labels, array_ops.size(predicted_scores)))
delta_z = tf.divide(delta_z, max_dcg)
delta_mask = tf.multiply(mask, delta_z)
delta_mask = tf.stop_gradient(delta_mask)
tmp_denominator = tf.matmul(delta_mask, tf.exp(predicted_scores))
multi_label_loss = tf.log(1 + tmp_denominator / tf.exp(predicted_scores))
permutation_loss = tf.reduce_sum(multi_label_loss)
permutation_loss /= which_rank
return permutation_loss
def get_lambda_residual(labels, predicted_scores):
tmp_labels = tf.cast(tf.squeeze(labels), tf.int32)
y, idx = tf.unique(tmp_labels)
which_rank = tf.shape(y)[0] - 1
which_rank = tf.cast(which_rank, tf.float32)
exp_predicted_scores = tf.exp(predicted_scores)
raw_pairwise_label_scores = scores.get_pairwise_scores(labels)
# gradient with respect to d
d_mask = masks.list_mask(raw_pairwise_label_scores)
# calculate delta_z
gains = 2**labels - 1
raw_pairwise_scores = scores.get_pairwise_scores(predicted_scores)
score_mask = masks.list_mask(raw_pairwise_scores)
ranks = tf.reduce_sum(score_mask, axis=1)
ranks = tf.reshape(ranks, [-1, 1])
log_2 = tf.log(tf.constant(2.0, dtype=tf.float32))
cg_discounts = tf.log(ranks + 2.0) / log_2
cg_discounts = tf.reshape(cg_discounts, [-1, 1])
raw_pairwise_discounts = tf.abs(scores.get_pairwise_scores(cg_discounts))
raw_pairwise_gains = tf.abs(scores.get_pairwise_scores(gains))
delta_z = raw_pairwise_gains
delta_z = tf.multiply(raw_pairwise_discounts, raw_pairwise_gains)
abs_pairwise_scores = tf.abs(raw_pairwise_scores)
delta_z = tf.divide(delta_z, abs_pairwise_scores+1e-7)
# max_dcg = tf.reduce_sum(math_fns.cal_idcg_ks(labels, 10))
max_dcg = tf.reduce_sum(math_fns.cal_idcg_ks(labels, array_ops.size(predicted_scores)))
delta_z = tf.divide(delta_z, max_dcg)
delta_d_mask = tf.multiply(d_mask, delta_z)
delta_d_mask = tf.stop_gradient(delta_d_mask)
tmp_denominator = tf.matmul(d_mask, exp_predicted_scores)
# loss_score_gradients
residuals = 1 / (1 + exp_predicted_scores / tmp_denominator)
gains = 2**labels - 1
# gain_sum = tf.reduce_sum(gains)
# residuals /= gain_sum
# residuals /= which_rank
# residuals /= num_not_min
# gradient with respect to d'
_tmp_denominator = tmp_denominator + exp_predicted_scores
_d_mask= masks.list_negative_mask(raw_pairwise_label_scores)
delta__d_mask = tf.multiply(_d_mask, delta_z)
delta__d_mask = tf.stop_gradient(delta__d_mask)
_residuals = tf.matmul(delta__d_mask, 1 / _tmp_denominator)
_residuals *= exp_predicted_scores
residuals += _residuals
residuals /= which_rank
return residuals
def _rlrank_pair():
pairwise_predicted_scores = scores.get_pairwise_scores(
predicted_scores)
pairwise_label_scores = scores.get_rl_pairwise_label_scores(labels)
loss = loss_fns.get_rlrank_pair_loss(pairwise_label_scores,
pairwise_predicted_scores, params)
return loss
def _lambdarank():
pairwise_predicted_scores = scores.get_pairwise_scores(predicted_scores)
pairwise_label_scores = scores.get_pairwise_label_scores(labels)
n_data = tf.shape(labels)[0]
swapped_ndcg = math_fns.cal_swapped_ndcg(labels,
predicted_scores, top_k_int=n_data)
loss = loss_fns.get_lambda_pair_loss(pairwise_label_scores, pairwise_predicted_scores,
params, swapped_ndcg)
return loss
def get_permutation_loss(labels, predicted_scores):
tmp_labels = tf.cast(tf.squeeze(labels), tf.int32)
y, idx = tf.unique(tmp_labels)
which_rank = tf.shape(y)[0] - 1
which_rank = tf.cast(which_rank, tf.float32)
raw_pairwise_label_scores = scores.get_pairwise_scores(labels)
mask = masks.list_mask(raw_pairwise_label_scores)
tmp_denominator = tf.matmul(mask, tf.exp(predicted_scores))
multi_label_loss = tf.log(1 + tmp_denominator / tf.exp(predicted_scores))
multi_label_loss *= 2**labels - 1
permutation_loss = tf.reduce_sum(multi_label_loss)
permutation_loss /= which_rank
return permutation_loss
def get_residual(labels, predicted_scores):
tmp_labels = tf.cast(tf.squeeze(labels), tf.int32)
y, idx = tf.unique(tmp_labels)
which_rank = tf.shape(y)[0] - 1
which_rank = tf.cast(which_rank, tf.float32)
exp_predicted_scores = tf.exp(predicted_scores)
raw_pairwise_label_scores = scores.get_pairwise_scores(labels)
# gradient with respect to d
d_mask = masks.list_mask(raw_pairwise_label_scores)
tmp_denominator = tf.matmul(d_mask, exp_predicted_scores)
# loss_score_gradients
residuals = 1 / (1 + exp_predicted_scores / tmp_denominator)
gains = 2**labels - 1
residuals *= gains
# gradient with respect to d'
_tmp_denominator = tmp_denominator + exp_predicted_scores
_d_mask= masks.list_negative_mask(raw_pairwise_label_scores)
_residuals = tf.matmul(_d_mask, gains / _tmp_denominator)
_residuals *= exp_predicted_scores
residuals += _residuals
residuals /= which_rank
return residuals
def _ranksvm():
pairwise_predicted_scores = scores.get_pairwise_scores(
predicted_scores)
pairwise_label_scores = scores.get_pairwise_label_scores(labels)
return loss_fns.get_hinge_loss(pairwise_label_scores,
pairwise_predicted_scores, params)
